Plugins

Interior and exterior ballistics paper reproduction and code generation skill. Requires LibreOffice Kit for PDF rendering and a vision API key (e.g. Qwen-VL) for formula OCR.

Structured image recognition: local sharp measurements plus a cloud vision model return description, OCR with coordinates, object locations and fine-grained attributes in one call; a fingerprint cache makes repeated images return in milliseconds without spending model quota.

Lets the agent drive Windows by sight: Windows OCR turns on-screen text into clickable targets, then clicks, drags, scrolls, types and presses keys, verifying each action with a per-pixel diff and an OCR text delta; F6+F7 is a separate emergency stop.

Registers the Akasha Book memory server over stdio and exposes its 22 akasha_* tools to DeepSeek Harness, covering lookup, briefs, revision chains, audits and session recall. Companions: Hindsight (repo memory), OpenViking (user memory), and the built-in session search (see repo README). Setup: clone the repo, copy core/ aside, then replace <AKASHA_DIR> in cordis.patch.yml with its absolute path (or set the AKASHA_DIR env var); plugins fail loudly while the placeholder remains.

Multi-model deliberation pipeline for DeepSeek Harness: independent proposals, blind peer review, program-executed evidence verification, immutable revision and independent acceptance on a fresh context.

Removes the model-capability gate that blocks image prompts: patches llm.resolveModelInfo results and adapter model tables to declare image input, so routes with no declared input modality can send and read images. No app.asar or config-tree edits.

Pixel-level screenshot diff for visual regression. Two local PNGs in; a machine-readable first line (`PIXEL_CHANGED=true|false`, greppable as an assertion), then the difference ratio, per-region bounding boxes, maximum channel delta and mean absolute error, plus a self-contained HTML comparison page. An ignore-region list with absolute or fractional coordinates excludes clocks, cursors and other per-render noise. It deliberately does no content recognition: it answers only "did this change", which is the part that can be made deterministic. Entirely local — no model call, no network, no upload.

Checks supervision logs for cross-day continuity and for on-site supervision coverage, against GB/T 50319-2013 and other published documents. Reports gaps and coverage differences for a human to review.

Vision through NVIDIA NIM: mounts the bundled stdio MCP server as mcp__opencode_eyes_nvidia__describe_image, which describes an image with any of seven hosted vision models (MiniMax-M3 by default) and rotates several API keys.

Eyes for text-only models: mounts the bundled stdio MCP server as mcp__opencode_eyes__describe_image, which sends an image file to StepFun step-3.7-flash and returns a detailed description.

Shows oversized images to the model at readable resolution instead of one downscaled copy: before admission, a large upload is split into as many tiles as the attachment store allows (an 8000x8000 screenshot becomes 20 tiles of 2000x1600), so small text survives. Replaces the local attachment store; adds no settings of its own.

Visual frontend revision workbench: point at a real page element, describe the change, send it into the current DSH session, then review fixed-viewport before/after screenshots (1280x800 and 390x844) beside a captured source diff, accepting each revision item by item. Revision records are kept per session under .dsh-revise/ and export as a single portable HTML report.

Persistent task supervision with plan coverage review, DAG progress, sidebar consultation, and stage/completion checks; npm development preview.

Local OCR using Apple Vision on macOS: text from screenshots, photos, and scans, with no install, no network and no API key.

个人解决经验记忆：保存问题、做法、来源和适用条件，下次相似任务可检索复用，并支持纠正与删除。搭配 dsh-bubble 提问、dsh-look 提供现场上下文。

Scene-awareness probe for DSH on macOS: a privacy-first, pull-model `look` tool that reads the frontmost non-self window (app info, AX title/selection, gated local OCR), plus a summon-hotkey snapshot captured the instant you press.

Personal-experience memory for DSH: turns solved tasks into sourced problem cases with condition-scoped claims, recalls them into new tasks via hidden pre-step injection, and makes corrections/deletes cascade to future recall.

Replaces the chat input model picker with a provider-grouped, collapsible list: groups models by provider, folds vision-bridge mirror providers back into their upstream, and carries the reasoning-effort options each model declares in settings. Tested on DSH Desktop only; the DSH web build is untested.

Large-image recognition for vision-exp models: lossless 800×800 tile recognition (smart/pipeline/full), local OCR with preprocessing & handwriting routing, optional multi-vendor GPU (DirectML/CUDA/OpenVINO) with auto CPU fallback.

DeepSeek Harness skill: build, rebuild and refresh a course module's revision atlas — a mind map whose leaves are paged notebooks, extracted faithfully from the module's markdown with machine-checked coverage and a human-gated plan.

Automatic tier-based model routing for DeepSeek Harness: a virtual `smart` model classifies each request by difficulty (hard / normal / easy) and by vision need, then delegates it to the models you already configured. 三级难度 + 视觉自动路由，虚拟 smart 模型零配置接入。

DSH plugin: matches a model you declared on a custom gateway to the capability metadata it should have — context window, output cap, reasoning levels and image support — by looking the bare model name up at runtime; the plugin never writes your settings document.

Turn the DeepSeek Harness Web UI into a real desktop application from inside DSH: one plugin that provisions a portable launcher (native WebView2 window, tray residency, silent backend start, readiness gating, debug console) plus desktop/start-menu shortc

Xiaomi MiMo-powered voice for DeepSeek Harness: browser 🎤/🧠/🔊 UI, voice_transcribe/voice_understand/voice_speak tools, configurable voice map (preset/voicedesign/voiceclone). Fork of zhuiyueya/dsh-voice (MIT), Settings pattern from Anionex/dsh-vision-toolkit (MIT).